00984nas a2200109   4500008004100000245004500041210004500086260003000131300001600161520065100177856004600828       1995                        eng d00aUnsupervised adaptive image segmentation0 aUnsupervised adaptive image segmentation  aPiscatawaybIEEEc1995///  a2399 - 24023 aThis paper deals with the problem of unsupervised Bayesian segmentation of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (Simulated Annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the available image only. Our approach consists of a recent iterative method of estimation, called Iterative Conditional Estimation (ICE), applied to a monogrid Markovian image segmentation model. The method has been tested on synthetic and real satellite images.  uhttps://home.inf.u-szeged.hu/en/node/1240